Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Evolutionary biology for the 21st century

New theoretical and conceptual frameworks are required for evolutionary biology to capitalize on the wealth of data now becoming available from the study of genomes, phenotypes, and organisms - including humans - in their natural environments.Molecular and Cellular BiologyOrganismic and Evolutionary Biolog

Applications of next-generation sequencing technologies and computational tools in molecular evolution and aquatic animals conservation studies : a short review

Aquatic ecosystems that form major biodiversity hotspots are critically threatened due to environmental and anthropogenic stressors. We believe that, in this genomic era, computational methods can be applied to promote aquatic biodiversity conservation by addressing questions related to the evolutionary history of aquatic organisms at the molecular level. However, huge amounts of genomics data generated can only be discerned through the use of bioinformatics. Here, we examine the applications of next-generation sequencing technologies and bioinformatics tools to study the molecular evolution of aquatic animals and discuss the current challenges and future perspectives of using bioinformatics toward aquatic animal conservation efforts

From parasite genomes to one healthy world: Are we having fun yet?

In 1990, the Human Genome Sequencing Project was established. This laid the ground work for an explosion of sequence data that has since followed. As a result of this effort, the first complete genome of an animal, Caenorhabditis elegans was published in 1998. The sequence of Drosophila melanogaster was made available in March, 2000 and in the following year, working drafts of the human genome were generated with the completed sequence (92%) being released in 2003. Recent advancements and next-generation technologies have made sequencing common place and have infiltrated every aspect of biological research, including parasitology. To date, sequencing of 32 apicomplexa and 24 nematode genomes are either in progress or near completion, and over 600k nematode EST and 200k apicomplexa EST submissions fill the databases. However, the winds have shifted and efforts are now refocusing on how best to store, mine and apply these data to problem solving. Herein we tend not to summarize existing X-omics datasets or present new technological advances that promise future benefits. Rather, the information to follow condenses up-to-date-applications of existing technologies to problem solving as it relates to parasite research. Advancements in non-parasite systems are also presented with the proviso that applications to parasite research are in the making

Educating Future Nursing Scientists: Recommendations for Integrating Omics Content in PhD Programs

Preparing the next generation of nursing scientists to conduct high-impact, competitive, sustainable, innovative, and interdisciplinary programs of research requires that the curricula for PhD programs keep pace with emerging areas of knowledge and health care/biomedical science. A field of inquiry that holds great potential to influence our understanding of the underlying biology and mechanisms of health and disease is omics. For the purpose of this article, omics refers to genomics, transcriptomics, proteomics, epigenomics, exposomics, microbiomics, and metabolomics. Traditionally, most PhD programs in schools of nursing do not incorporate this content into their core curricula. As part of the Council for the Advancement of Nursing Science\u27s Idea Festival for Nursing Science Education, a work group charged with addressing omics preparation for the next generation of nursing scientists was convened. The purpose of this article is to describe key findings and recommendations from the work group that unanimously and enthusiastically support the incorporation of omics content into the curricula of PhD programs in nursing. The work group also calls to action faculty in schools of nursing to develop strategies to enable students needing immersion in omics science and methods to execute their research goals

From bench to bountiful harvests : a road map for the next decade of Arabidopsis research

In the face of an increasing world population and climate instability, the demands for food and fuel will continue to rise. Plant science will be crucial to help meet these exponentially increasing requirements for food and fuel supplies. Fundamental plant research will play a major role in providing key advances in our understanding of basic plant processes that can then flow into practical advances through knowledge sharing and collaborations. The model plant Arabidopsis thaliana has played a major role in our understanding of plant biology, and the Arabidopsis community has developed many tools and resources to continue building on this knowledge. Drawing from previous experience of internationally coordinated projects, The international Arabidopsis community, represented by the Multinational Arabidopsis Steering Committee (MASC), has drawn up a road map for the next decade of Arabidopsis research to inform scientists and decision makers on the future foci of Arabidopsis research within the wider plant science landscape. This article provides a summary of the MASC road map

A Comprehensive Approach for the Conceptual Modeling of Genomic Data

The human genome is traditionally represented as a DNA sequence of three billion base pairs. However, its intricacies are captured by many more complex signals, representing DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data formats is used to represent such signals. Different conceptual models explain such elaborate structure and behavior. Among them, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior – independent of data formats. The Genomic Conceptual Model (GCM) instead provides a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. We hereby propose to join these two approaches to achieve a more complete vision, linking (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. The link is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual, as data records can be semantically described by high-level concepts and exploit their links. In turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. As a result, it will be possible to express queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. The approach is here exemplified using the DNA variation data type but is applicable to all genomic information